About run_analysis.R

This script:

1. Merges the training and the test sets to create one data set.
2. Extracts only the measurements on the mean and standard deviation for each measurement.
3. Uses descriptive activity names to name the activities in the data set.
4. Appropriately labels the data set with descriptive variable names.
5. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.

How it works

Each group of data sets is contained in 3 tables (subject_*,X_* and y_* where *=training or test); variable names are contained in features.txt and activity names are contained in activity_labels.txt. The output of each step is used in the proceeding step. The tidy data set at the end of step 5 is called data5 and is in wide format. (See references below for conditions of a tidy data).

STEP 1

• load and clean column names from features.txt (to enable select function in step 2; it also completes most of step 4).
• Column bind the 3 tables for training and test seperately.
• Row bind the training and test tables to create one data set
• Names for columns of X_* tables come from features.txt, so load that first.
• OUTPUT: data1

STEP 2

• create vector of column names to include; by strict name match with '_mean_' and '_std_'. (This is based on a strict search for '-mean()' and '-std()', a looser search (say on 'mean' and 'std') would produce more variables).
• create extract using select_ function (which allows for selection based on vector of column names).
• OUTPUT: data2

STEP 3

• Use activity_labels.txt for activity names.
• Read in activity names and merge with data set.
• OUTPUT: data3

STEP 4

• The 'illegal' characters such as '-', ',', '(', ')' were already replaced in step 1.
• Remove last underscore '_' if it exists.
• The original names, after replacing 'illegal' characters, are interpreted to be sufficiently descriptive in combination with CodeBook.md.
• OUTPUT: data3 (renamed)

STEP 5

• Use dplyr pipe commands to group by activity and subject variables and then summarise by mean.
• The data set is tidy as per references below.
• OUTPUT: data5

References

Tidy data set:

• https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html
• https://thoughtfulbloke.wordpress.com/2015/09/09/getting-and-cleaning-the-assignment/